Benjamin Fernandez Guzman

National Distance Education University | UNED · Department of Computer Science and Automatic Control

The dynamics of a skid-steering robot present intrinsic non-linearities that make the design and implementation of a controller a very complex task, time consuming and difficult to implement into an embedded system with limited resources. This paper presents a simplified first order digital model approximation and an optimal observer-based control...

There are two main issues to consider when designing a controller for autonomous off-road vehicles: velocity and terrain irregularities. Whereas the first one is measurable, the second one is very difficult to determine. Solutions to cover these issues could be very complex and difficult to implement in an embedded system with limited resources. Th...

Due to the changes in the soil, speed and hitch forces, the dynamics of a farm tractor are constantly changing making the design of an autonomous lane-tracking controller a very complex task. To be able to react to those changes, this paper presents a new adaptive system based on a self-tuning regulator made up of a recursive least-squares paramete...

This article presents a Tractor-Implement Simulation using C++, openFrameworks and OpenGL. It provides 3D animation and a friendly user interface for online interaction. This facilitates understanding, learning, design and control tuning of off-road guidance and navigation systems.

A neural network approach for the identification of non-linear feedback models of hysteresis.In this article, the non-linearity can be identified with a two-layer perceptron network. The linear part of the model can also be constructed together with the perceptron network for the identification of the hysteresis model.

A neural network approach for the identification of nonlinear feedback models of hysteresis.In this article, the nonlinearity can be identified with a two-layer perceptron network. The linear part of the model can also be constructed together with the perceptron network for the identification of the hysteresis model.

A neural network approach for the identification of nonlinear feedback models of hysteresis.In this article, the nonlinearity can be identified with a two-layer perceptron network. The linear part of the model can also be constructed together with the perceptron network for the identification of the hysteresis model.